PyTorch implementation of UNet++ (Nested U-Net).

Related tags

Deep Learningpytorchimage-segmentationunet
Overview

PyTorch implementation of UNet++ (Nested U-Net)

MIT License

This repository contains code for a image segmentation model based on UNet++: A Nested U-Net Architecture for Medical Image Segmentation implemented in PyTorch.

[NEW] Add support for multi-class segmentation dataset.

[NEW] Add support for PyTorch 1.x.

Requirements

  • PyTorch 1.x or 0.41

Installation

  1. Create an anaconda environment.
conda create -n=<env_name> python=3.6 anaconda
conda activate <env_name>
  1. Install PyTorch.
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
  1. Install pip packages.
pip install -r requirements.txt

Training on 2018 Data Science Bowl dataset

  1. Download dataset from here to inputs/ and unzip. The file structure is the following:
inputs
└── data-science-bowl-2018
    ├── stage1_train
    |   ├── 00ae65...
    │   │   ├── images
    │   │   │   └── 00ae65...
    │   │   └── masks
    │   │       └── 00ae65...            
    │   ├── ...
    |
    ...
  1. Preprocess.
python preprocess_dsb2018.py
  1. Train the model.
python train.py --dataset dsb2018_96 --arch NestedUNet
  1. Evaluate.
python val.py --name dsb2018_96_NestedUNet_woDS

(Optional) Using LovaszHingeLoss

  1. Clone LovaszSoftmax from bermanmaxim/LovaszSoftmax.
git clone https://github.com/bermanmaxim/LovaszSoftmax.git
  1. Train the model with LovaszHingeLoss.
python train.py --dataset dsb2018_96 --arch NestedUNet --loss LovaszHingeLoss

Training on original dataset

Make sure to put the files as the following structure (e.g. the number of classes is 2):

inputs
└── <dataset name>
    ├── images
    |   ├── 0a7e06.jpg
    │   ├── 0aab0a.jpg
    │   ├── 0b1761.jpg
    │   ├── ...
    |
    └── masks
        ├── 0
        |   ├── 0a7e06.png
        |   ├── 0aab0a.png
        |   ├── 0b1761.png
        |   ├── ...
        |
        └── 1
            ├── 0a7e06.png
            ├── 0aab0a.png
            ├── 0b1761.png
            ├── ...
  1. Train the model.
python train.py --dataset <dataset name> --arch NestedUNet --img_ext .jpg --mask_ext .png
  1. Evaluate.
python val.py --name <dataset name>_NestedUNet_woDS

Results

DSB2018 (96x96)

Here is the results on DSB2018 dataset (96x96) with LovaszHingeLoss.

Model IoU Loss
U-Net 0.839 0.365
Nested U-Net 0.842 0.354
Nested U-Net w/ Deepsupervision 0.843 0.362
Owner
4ui_iurz1
4ui_iurz1
GitHub Repository
[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

EPCDepth EPCDepth is a self-supervised monocular depth estimation model, whose supervision is coming from the other image in a stereo pair. Details ar

Rui Peng 110 Dec 23, 2022
[ACL 2022] LinkBERT: A Knowledgeable Language Model 😎 Pretrained with Document Links

LinkBERT: A Knowledgeable Language Model Pretrained with Document Links This repo provides the model, code & data of our paper: LinkBERT: Pretraining

Michihiro Yasunaga 264 Jan 01, 2023
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
Video Representation Learning by Recognizing Temporal Transformations. In ECCV, 2020.

Video Representation Learning by Recognizing Temporal Transformations [Project Page] Simon Jenni, Givi Meishvili, and Paolo Favaro. In ECCV, 2020. Thi

Simon Jenni 46 Nov 14, 2022
Pcos-prediction - Predicts the likelihood of Polycystic Ovary Syndrome based on patient attributes and symptoms

PCOS Prediction 🥼 Predicts the likelihood of Polycystic Ovary Syndrome based on

Samantha Van Seters 1 Jan 10, 2022
Practical and Real-world applications of ML based on the homework of Hung-yi Lee Machine Learning Course 2021

Machine Learning Theory and Application Overview This repository is inspired by the Hung-yi Lee Machine Learning Course 2021. In that course, professo

SilenceJiang 35 Nov 22, 2022
FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control

FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control by Dimitri von Rütte, Luca Biggio, Yannic Kilcher, Thomas Hofmann FIGARO: Generat

Dimitri 83 Jan 07, 2023
Exploring Classification Equilibrium in Long-Tailed Object Detection, ICCV2021

Exploring Classification Equilibrium in Long-Tailed Object Detection (LOCE, ICCV 2021) Paper Introduction The conventional detectors tend to make imba

52 Nov 21, 2022
Efficient Training of Visual Transformers with Small Datasets

Official codes for "Efficient Training of Visual Transformers with Small Datasets", NerIPS 2021.

Yahui Liu 112 Dec 25, 2022
Pytorch implementation of the paper SPICE: Semantic Pseudo-labeling for Image Clustering

SPICE: Semantic Pseudo-labeling for Image Clustering By Chuang Niu and Ge Wang This is a Pytorch implementation of the paper. (In updating) SOTA on 5

Chuang Niu 154 Dec 15, 2022
Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems.

CottonWeeds Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems. requirements pytorch torchsumma

Dong Chen 8 Jun 07, 2022
TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision

TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{you2019torchcv, author = {Ansheng You and Xiangtai Li and Zhen Zhu a

Donny You 2.2k Jan 06, 2023
make ASCII Art by Deep Learning

DeepAA This is convolutional neural networks generating ASCII art. This repository is under construction. This work is accepted by NIPS 2017 Workshop,

OsciiArt 1.4k Dec 28, 2022
Python package for dynamic system estimation of time series

PyDSE Toolset for Dynamic System Estimation for time series inspired by DSE. It is in a beta state and only includes ARMA models right now. Documentat

Blue Yonder GmbH 40 Oct 07, 2022
Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

TableauBits 3 May 29, 2022
Deep Reinforcement Learning with pytorch & visdom

Deep Reinforcement Learning with pytorch & visdom Sample testings of trained agents (DQN on Breakout, A3C on Pong, DoubleDQN on CartPole, continuous A

Jingwei Zhang 783 Jan 04, 2023
PyTorch implementation of SmoothGrad: removing noise by adding noise.

SmoothGrad implementation in PyTorch PyTorch implementation of SmoothGrad: removing noise by adding noise. Vanilla Gradients SmoothGrad Guided backpro

SSKH 143 Jan 05, 2023
Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(

Phil Wang 180 Jan 05, 2023
Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch.

SE3 Transformer - Pytorch Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch. May be needed for replicating Alphafold2 resu

Phil Wang 207 Dec 23, 2022
Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Dominik Klein 189 Dec 21, 2022